
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import warnings
warnings.filterwarnings('ignore')


print (os.getcwd())

C:\Users\Dell


os.chdir ('C:\\Users\\Dell\\OneDrive\\Desktop\\CAR Price Prediction')


print (os.getcwd())

C:\Users\Dell\OneDrive\Desktop\CAR Price Prediction


data=pd.read_csv("C:\\Users\\Dell\\OneDrive\\Desktop\\excel books\\audi.csv")
display(data)


import pandas_profiling as pp


display(pp.ProfileReport(data))

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]


print(len(data))

10668


print(data.shape)

(10668, 9)


display (data.dtypes )

model              object
year                int64
price               int64
transmission       object
dist_travelled      int64
fuelType           object
tax                 int64
mpg               float64
engineSize        float64
dtype: object


display (data.isna().sum() )

model             0
year              0
price             0
transmission      0
dist_travelled    0
fuelType          0
tax               0
mpg               0
engineSize        0
dtype: int64


print (data.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10668 entries, 0 to 10667
Data columns (total 9 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   model           10668 non-null  object 
 1   year            10668 non-null  int64  
 2   price           10668 non-null  int64  
 3   transmission    10668 non-null  object 
 4   dist_travelled  10668 non-null  int64  
 5   fuelType        10668 non-null  object 
 6   tax             10668 non-null  int64  
 7   mpg             10668 non-null  float64
 8   engineSize      10668 non-null  float64
dtypes: float64(2), int64(4), object(3)
memory usage: 750.2+ KB
None


display (data.describe ())


data


data.drop_duplicates(subset=['model','year','price','transmission','dist_travelled','fuelType','tax','mpg','engineSize'],inplace=True,keep='first')


data.shape

(10565, 9)


X = data.iloc[:,[0,1,3,4,5,6,7,8]].values
display (X.shape)
display (X)

(10565, 8)

array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
       [' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
       [' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)


data.head()


Y = data.iloc[:,[2]].values
display (Y.shape)
display (Y)

(10565, 1)

array([[12500],
       [16500],
       [11000],
       ...,
       [17199],
       [19499],
       [15999]], dtype=int64)


display(pd.DataFrame(X).head(5))


from sklearn.preprocessing import LabelEncoder


le1 = LabelEncoder()
X[:,0] = le1.fit_transform(X[:,0])
le2 = LabelEncoder()
X[:,-4] = le2.fit_transform(X[:,-4])
display (X)

array([[0, 2017, 'Manual', ..., 150, 55.4, 1.4],
       [5, 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [0, 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [2, 2020, 'Manual', ..., 150, 49.6, 1.0],
       [9, 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [9, 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)


from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers = [('encoder',OneHotEncoder(),[2])],remainder='passthrough')
X = ct.fit_transform(X)
display (X.shape)
display (pd.DataFrame(X))

(10565, 10)


from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
display (pd.DataFrame(X))


from sklearn.model_selection import train_test_split

(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)


print (X_train.shape, Y_train.shape)
print(Y_test.shape)

(8452, 10) (8452, 1)
(2113, 1)


from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train,Y_train)

LinearRegression()


y_pred = reg.predict(X_test)
display (y_pred.shape)

(2113, 1)


print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))

[[31863.18288911 34991.        ]
 [19374.13511826 17299.        ]
 [13295.65796165 11444.        ]
 ...
 [18373.48622929 17670.        ]
 [20230.20801119 14290.        ]
 [17652.48622929 18990.        ]]


from sklearn.metrics import r2_score,mean_absolute_error
print('R2 Score ', r2_score(Y_test, y_pred))
print('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))

R2 Score  0.7941818440937328
Mean Absolute Error 3244.810815892843


y_pred = reg.predict(X)
display (y_pred)
y_pred=y_pred[:2113,0]

array([[14861.39031251],
       [20407.69760853],
       [13617.39031251],
       ...,
       [19728.17779394],
       [21238.75145114],
       [16811.25145114]])


result = pd.concat([data,pd.DataFrame(y_pred)],axis=1)
display( result.head())
result.rename(columns={0: 'pred_price'}, inplace=True)
display( result)


data


actual=data.iloc[:2113,2]
actual
distance=data.iloc[:2113,4]
print(distance)

0       15735
1       36203
2       29946
3       25952
4        1998
        ...  
2121    52595
2122    25000
2123    31292
2124    49652
2125    15016
Name: dist_travelled, Length: 2113, dtype: int64


print(distance.shape)
print(y_pred.shape)

(2113,)
(2113,)


%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()


from sklearn.ensemble import RandomForestRegressor
regression = RandomForestRegressor(random_state=0)
regression.fit(X_train,Y_train)
display (regression)

RandomForestRegressor(random_state=0)


from sklearn.model_selection import train_test_split

(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)


print (X_train.shape, Y_train.shape)
print (X_test.shape, Y_test.shape)

(8452, 10) (8452, 1)
(2113, 10) (2113, 1)


y_pred = regression.predict(X_test)
display (y_pred)

array([34565.81, 16820.73, 11530.84, ..., 18497.45, 17153.97, 18620.66])


result = pd.concat([data,pd.DataFrame(y_pred)],axis=1)
display( result.tail(50))
result.rename(columns={0: 'pred_price'}, inplace=True)
display( result)


from sklearn.metrics import r2_score,mean_absolute_error
print  ('R2 Score ', r2_score(Y_test, y_pred))
print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))

R2 Score  0.9587306545197221
Mean Absolute Error 1517.4627789258122


%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()


from sklearn.ensemble import  ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators=120)
ET_Model.fit(X_train,Y_train)
y_predict=ET_Model.predict(X_test)
import numpy as np


from sklearn.metrics import r2_score,mean_absolute_error
print('R2 Score :', r2_score(Y_test, y_predict))
print ('Mean Absolute Error:', mean_absolute_error(Y_test,y_predict))

R2 Score : 0.9625678491519055
Mean Absolute Error: 1517.800980044171


'''y_pred = reg.predict(X)
display (y_pred)
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1).head()
display( result)'''

'y_pred = reg.predict(X)\ndisplay (y_pred)\nresult = pd.concat([data,pd.DataFrame(y_pred)],axis=1).head()\ndisplay( result)'


%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,Y_test,color='red')
plt.scatter(distance,y_predict,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()


### Randomized search CV


from sklearn.model_selection import RandomizedSearchCV


n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]


max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]


rand_grid={'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf}


rf=RandomForestRegressor()


rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)


rCV.fit(X_train,Y_train)

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')


rf_pred=rCV.predict(X_test)
display (rf_pred)

array([34363.68578869, 16651.55211445, 11707.03009425, ...,
       18883.21945933, 16724.75648133, 18457.28272006])


rf_pred=rCV.predict(X_test)
display (rf_pred)

array([34363.68578869, 16651.55211445, 11707.03009425, ...,
       18883.21945933, 16724.75648133, 18457.28272006])


from sklearn.metrics import mean_absolute_error,mean_squared_error
print('MAE',mean_absolute_error(Y_test,rf_pred))
print('MSE',mean_squared_error(Y_test,rf_pred))

MAE 1505.2773269897075
MSE 6100595.4818421


display (r2_score(Y_test,rf_pred))

0.9580830027921906


%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,rf_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()


from catboost import CatBoostRegressor
cat=CatBoostRegressor()
print (cat.fit(X_train,Y_train))

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<catboost.core.CatBoostRegressor object at 0x000001B09DF8F8E0>


cat_pred=cat.predict(X_test)
display (cat_pred)

array([34403.2885362 , 18068.56325027, 11918.19730173, ...,
       18621.48599089, 16917.10863635, 18220.58207119])


display (r2_score(Y_test,cat_pred))
print('MAE',mean_absolute_error(Y_test,cat_pred))
print('MSE', mean_squared_error(Y_test,cat_pred))

0.9621533622246573

MAE 1440.7720299246405
MSE 5508195.786795388


plt.plot(cat_pred,label='pred')
plt.plot(Y_test,label='Actual')
plt.legend('a','b')
plt.show()


%matplotlib inline
plt.figure(figsize=(18,5))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,cat_pred,color='blue')
plt.title('ACTUAL PRICE VS PREDICTED PRICE')
plt.legend(['Actual price ','Predicted Price'])
plt.show()


'''from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error'''

'from sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.metrics import mean_squared_error'


#X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)


#knn_regressor = KNeighborsRegressor(n_neighbors=4)


#knn_regressor.fit(X_train, y_train)


#y_pred = knn_regressor.predict(X_test)
#print(y_pred)


#mse = mean_squared_error(y_test, y_pred)
#print(f'Mean Squared Error: {mse}')


#from sklearn.metrics import r2_score,mean_absolute_error
#print  ('R2 Score ', r2_score(Y_test, y_pred))
#print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))


'''%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()'''

## knn regressor is not good

"%matplotlib inline\nplt.figure(figsize=(10, 6))\nplt.xlabel('dist_travlled')\nplt.ylabel('price')\nplt.scatter(distance,actual,color='red')\nplt.scatter(distance,y_pred,color='blue')\n\nplt.legend(['Actual price ','Predicted Price'])\nplt.show()"


from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, r2_score


decision_tree_regressor = DecisionTreeRegressor(random_state=42)


decision_tree_regressor.fit(X_train, Y_train)

DecisionTreeRegressor(random_state=42)


y_pred = decision_tree_regressor.predict(X_test)


mse = mean_squared_error(Y_test, y_pred)
r2 = r2_score(Y_test, y_pred)
print('MAE',mean_absolute_error(Y_test,y_pred))

MAE 1921.797128884682


print(r2)

0.9337260347094475


%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()

	year	price	dist_travelled	tax	mpg	engineSize
count	10668.000000	10668.000000	10668.000000	10668.000000	10668.000000	10668.000000
mean	2017.100675	22896.685039	24827.244001	126.011436	50.770022	1.930709
std	2.167494	11714.841888	23505.257205	67.170294	12.949782	0.602957
min	1997.000000	1490.000000	1.000000	0.000000	18.900000	0.000000
25%	2016.000000	15130.750000	5968.750000	125.000000	40.900000	1.500000
50%	2017.000000	20200.000000	19000.000000	145.000000	49.600000	2.000000
75%	2019.000000	27990.000000	36464.500000	145.000000	58.900000	2.000000
max	2020.000000	145000.000000	323000.000000	580.000000	188.300000	6.300000

	0	1	2	3	4	5	6	7	8	9
0	-0.582997	1.203038	-0.714096	-1.119276	-0.039002	-0.393254	1.053589	0.357402	0.351966	-0.884062
1	1.715274	-0.831229	-0.714096	-0.158819	-0.500425	0.479662	-0.951665	-1.571222	1.030836	0.111173
2	-0.582997	1.203038	-0.714096	-1.119276	-0.500425	0.212815	1.053589	-1.422867	0.351966	-0.884062
3	1.715274	-0.831229	-0.714096	-0.543002	-0.039002	0.042479	-0.951665	0.283224	1.269983	0.111173
4	-0.582997	1.203038	-0.714096	-0.735093	0.883845	-0.979108	1.053589	0.283224	-0.095471	-1.547551
...	...	...	...	...	...	...	...	...	...	...
10560	-0.582997	1.203038	-0.714096	-0.735093	1.345269	-0.892959	1.053589	0.283224	-0.095471	-1.547551
10561	-0.582997	1.203038	-0.714096	-0.735093	1.345269	-0.979961	1.053589	0.357402	-0.095471	-1.547551
10562	-0.582997	1.203038	-0.714096	-0.735093	1.345269	-1.038346	1.053589	0.357402	-0.095471	-1.547551
10563	1.715274	-0.831229	-0.714096	0.609547	-0.039002	-0.695585	1.053589	0.357402	-0.226616	-0.884062
10564	-0.582997	1.203038	-0.714096	0.609547	-0.500425	-0.558728	1.053589	0.357402	-0.226616	-0.884062

	model	year	price	transmission	dist_travelled	fuelType	tax	mpg	engineSize	0
0	A1	2017.0	12500.0	Manual	15735.0	Petrol	150.0	55.4	1.4	14861.390313
1	A6	2016.0	16500.0	Automatic	36203.0	Diesel	20.0	64.2	2.0	20407.697609
2	A1	2016.0	11000.0	Manual	29946.0	Petrol	30.0	55.4	1.4	13617.390313
3	A4	2017.0	16800.0	Automatic	25952.0	Diesel	145.0	67.3	2.0	20163.341671
4	A3	2019.0	17300.0	Manual	1998.0	Petrol	145.0	49.6	1.0	17648.177794

	model	year	price	transmission	dist_travelled	fuelType	tax	mpg	engineSize	pred_price
0	A1	2017.0	12500.0	Manual	15735.0	Petrol	150.0	55.4	1.4	14861.390313
1	A6	2016.0	16500.0	Automatic	36203.0	Diesel	20.0	64.2	2.0	20407.697609
2	A1	2016.0	11000.0	Manual	29946.0	Petrol	30.0	55.4	1.4	13617.390313
3	A4	2017.0	16800.0	Automatic	25952.0	Diesel	145.0	67.3	2.0	20163.341671
4	A3	2019.0	17300.0	Manual	1998.0	Petrol	145.0	49.6	1.0	17648.177794
...	...	...	...	...	...	...	...	...	...	...
1162	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	20095.841671
1563	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7677.746250
1564	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	22037.069150
1874	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	21134.659596
1875	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	13741.697609

	model	year	price	transmission	dist_travelled	fuelType	tax	mpg	engineSize	0
10631	TT	2012.0	10490.0	Manual	24693.0	Diesel	165.0	51.4	2.0	NaN
10632	A1	2010.0	9990.0	Automatic	38000.0	Petrol	125.0	53.3	1.4	NaN
10633	A4	2018.0	26891.0	Automatic	22414.0	Petrol	145.0	36.7	3.0	NaN
10634	Q7	2017.0	45595.0	Automatic	28949.0	Diesel	145.0	39.2	4.0	NaN
10635	A3	2016.0	18000.0	Automatic	29494.0	Petrol	125.0	49.6	2.0	NaN
10636	A1	2013.0	9291.0	Manual	29382.0	Petrol	125.0	53.3	1.4	NaN
10637	A5	2017.0	21291.0	Automatic	29666.0	Diesel	30.0	65.7	2.0	NaN
10638	A4	2017.0	18491.0	Automatic	17900.0	Petrol	145.0	50.4	1.4	NaN
10639	A6	2020.0	28000.0	Automatic	2511.0	Diesel	145.0	47.9	2.0	NaN
10640	Q5	2020.0	37000.0	Automatic	1436.0	Petrol	145.0	32.1	2.0	NaN
10641	A5	2020.0	25000.0	Automatic	751.0	Petrol	145.0	40.4	2.0	NaN
10642	Q5	2019.0	33000.0	Automatic	5207.0	Diesel	145.0	38.2	2.0	NaN
10643	A4	2019.0	30000.0	Automatic	9900.0	Diesel	145.0	49.6	2.0	NaN
10644	A5	2019.0	25000.0	Automatic	8571.0	Diesel	145.0	46.3	2.0	NaN
10645	A1	2016.0	10999.0	Manual	22150.0	Diesel	0.0	76.3	1.6	NaN
10646	A1	2016.0	12380.0	Manual	40119.0	Petrol	30.0	55.4	1.4	NaN
10647	A3	2015.0	21000.0	Automatic	12084.0	Petrol	205.0	39.8	2.0	NaN
10648	RS6	2016.0	49990.0	Automatic	24000.0	Petrol	325.0	29.4	4.0	NaN
10649	A3	2009.0	3750.0	Manual	120000.0	Diesel	145.0	53.3	2.0	NaN
10650	A4	2011.0	6995.0	Manual	88000.0	Diesel	30.0	61.4	2.0	NaN
10651	A3	2011.0	9695.0	Manual	32300.0	Petrol	235.0	39.2	2.0	NaN
10652	A1	2014.0	9995.0	Manual	54000.0	Petrol	30.0	55.4	1.2	NaN
10653	A3	2017.0	12995.0	Manual	23820.0	Petrol	145.0	60.1	1.0	NaN
10654	A3	2016.0	16495.0	Semi-Auto	46600.0	Diesel	125.0	57.6	2.0	NaN
10655	S4	2018.0	29995.0	Automatic	29000.0	Petrol	150.0	35.8	3.0	NaN
10656	A3	2016.0	15495.0	Semi-Auto	52500.0	Hybrid	0.0	176.6	1.4	NaN
10657	A4	2016.0	20995.0	Semi-Auto	23700.0	Diesel	30.0	61.4	2.0	NaN
10658	A3	2016.0	14995.0	Manual	39750.0	Petrol	30.0	57.6	1.4	NaN
10659	A6	2018.0	27995.0	Semi-Auto	27500.0	Petrol	150.0	39.8	2.0	NaN
10660	A4	2011.0	9995.0	Automatic	78000.0	Diesel	305.0	39.8	3.0	NaN
10661	A4	2011.0	6995.0	Manual	95000.0	Diesel	145.0	53.3	2.0	NaN
10662	A3	2013.0	12695.0	Manual	31500.0	Petrol	125.0	53.3	1.4	NaN
10663	A3	2020.0	16999.0	Manual	4018.0	Petrol	145.0	49.6	1.0	NaN
10664	A3	2020.0	16999.0	Manual	1978.0	Petrol	150.0	49.6	1.0	NaN
10665	A3	2020.0	17199.0	Manual	609.0	Petrol	150.0	49.6	1.0	NaN
10666	Q3	2017.0	19499.0	Automatic	8646.0	Petrol	150.0	47.9	1.4	NaN
10667	Q3	2016.0	15999.0	Manual	11855.0	Petrol	150.0	47.9	1.4	NaN
273	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	21563.460
764	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	20608.700
784	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	31939.952
967	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	119217.800
990	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6965.660
1133	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12045.820
1137	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	17817.460
1146	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	21198.890
1162	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	44255.550
1563	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	32483.820
1564	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	33188.750
1874	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12127.800
1875	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	25382.800

importing required files¶

reading data¶

manual data Exploration¶

Label Encoding¶

one hot encoding¶

Standardizing the data¶

spliting data to train and test the model¶

using regression¶

checking for accuracy¶

using random forest regressor¶

checking for accuracy¶

using extra tree Regressor¶

checking for Accuracy¶

using randomized search cv on Random forest regressor¶

checking for accuracy¶

using Cat boost¶

using knn regressor¶

decision tree regressor¶

finding accuracy¶

Conclusion¶

Random forest and decision tree are giving high accuracy than linear and knn regressor and desicion tree regressor is also showing better result, and also done randomized search CV for randomforest model by taking parameters randomly also gave good results¶

	model	year	price	transmission	dist_travelled	fuelType	tax	mpg	engineSize
0	A1	2017	12500	Manual	15735	Petrol	150	55.4	1.4
1	A6	2016	16500	Automatic	36203	Diesel	20	64.2	2.0
2	A1	2016	11000	Manual	29946	Petrol	30	55.4	1.4
3	A4	2017	16800	Automatic	25952	Diesel	145	67.3	2.0
4	A3	2019	17300	Manual	1998	Petrol	145	49.6	1.0
...	...	...	...	...	...	...	...	...	...
10663	A3	2020	16999	Manual	4018	Petrol	145	49.6	1.0
10664	A3	2020	16999	Manual	1978	Petrol	150	49.6	1.0
10665	A3	2020	17199	Manual	609	Petrol	150	49.6	1.0
10666	Q3	2017	19499	Automatic	8646	Petrol	150	47.9	1.4
10667	Q3	2016	15999	Manual	11855	Petrol	150	47.9	1.4